Reading minds-controlling minds

4 Reading minds/controlling
minds
Much of the interest and anxiety provoked by new and developing
neuroscientific technologies is centered around two issues: the
extent to which these technologies might allow their users to read
the thoughts of people, and (as if that prospect was not disturbing
enough) the extent to which these technologies might actually be
used to control people. Some commentators believe that one or both
of these issues are pressing, in the sense that the relevant technolo-
gies will soon be available; some even believe that these technologies
already exist. In this chapter, we will ask how worried we should be.
Are these technologies imminent? And if they are, are they as
threatening as they appear?
mind reading and mind controlling
There has been a great deal of interest in the possibility of ‘‘brain
reading’’ as a lie detection technology. The problems with existing lie
detectors are well known: they produce high rates both of false
positives and of false negatives, and they can be ‘‘beaten’’ by people
who deliberately heighten their responses to control questions,
which are used to establish a baseline for comparison. In its overview
of current lie-detection techniques, the US National Research
Council concluded that there is ‘‘little basis for the expectation that
a polygraph test could have extremely high accuracy’’ (National
Research Council 2003: 212). The reasons for this conclusion are
many: because the responses measured are not uniquely involved in
deception, because they include responses that can be deliberately
controlled and because the technology is difficult to implement in
the real-world. The authors conclude that further research and
investment can be expected to produce only modest gains in accu-
racy. For these kinds of reasons, conventional polygraph results are
inadmissible as evidence in most jurisdictions.

It is recognition of the severe limitations of current lie
detection technologies that is responsible, in important part, for the
current interest in lie detectors that directly ‘‘read’’ the mind.
Polygraph machines are sensitive to changes in somatic states such
as skin conductance (changes in the resistance of the skin to elec-
tricity, which is a good indicator of increased sweating), heart rate
and blood pressure. Now, the problem with these measures is that,
at best, they are only correlated with deliberate deception. They are
indications of increased nervousness, and can be manipulated.
Moreover, even with a naı
¨
ve subject who does not know how to
manipulate these responses, the correlation between the responses
and deliberate deception is far from perfect; hence the false positives
(where these responses peak but the subject is not lying), and the
false negatives (where the subject is lying, but their somatic
responses do not reflect this fact). Proponents of neurologically
based lie detection argue, apparently plausibly, that such technolo-
gies would not be subject to these limitations. It might be possible
to fool a system that measures only stand-ins for lies, but it is not
possible to fool a system capable of honing in on the lies them-
selves. Of course, lies are not directly detectable, but the technology
might be capable of doing the next best thing. Thoughts are realized
neurologically; in the jargon of cognitive science, they have neural
correlates. The correlation between the lie and its neural correlate
is, by definition, perfect. Hence, if the lie detection technology
can hone in on the neural correlates of lies, it will not give false
positives or false negatives, and it will not be able to be fooled.
Brains do not lie.
The problem with this line of thought is obvious. Though it is

certainly true that every thought has a neural correlate – to say that is
just to say that thoughts are realized in brains, so the claim should
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be uncontroversial to anyone who does not believe in substance
dualism – it does not follow that for every type of thought there is a
distinct neural correlate. Indeed, the latter claim is rather implau-
sible, given that there are endless ways in which thoughts might be
categorized (‘‘concerning animals with fewer than four legs,’’ ‘‘con-
cerning either my maiden aunt or a ming vase,’’ ‘‘concerning an odd
number of elephants’’ – the categories are limited only by our inge-
nuity). But the idea that we can detect lies by honing in on their
neural correlates requires that to the thought-type ‘‘deliberate
deception’’ there corresponds one, or at any rate a tractable number,
of distinct neural correlates. Unless and until we develop the tech-
nology to translate neural correlates into thoughts (a prospect we
shall consider later in this chapter), we can only hope to detect lies
if we discover that there is something distinctive about deliberate
deception; something we can look for in brain scans.
So reliable neurological detection depends upon there being
something neurologically distinctive about deliberate deception. Is
there? There are various proposals for such distinctive correlates,
either of the entire class of deliberate deception, or of various smaller
segments of that class. Perhaps the best-known technique of neuro-
logical lie detection, ‘‘brain fingerprinting,’’ targets one possible
distinctive correlate. Brain fingerprinting does not aim to detect any
and all deliberate deception; instead, it is aimed at detecting guilty
knowledge. The technology uses memory and encoding related
multifaceted electroencephalographic response (MERMER), which
combines electroencephalography (EEG) data from several sites on

the scalp. Electrodes are attached to the subject’s scalp at several
sites, and brain activity is measured while the subject is asked
questions or shown pictures. If the subject recognizes a picture or
is familiar with the content of the question, they are supposed to
exhibit a characteristic response, the P300 wave (so called because
it occurs 300 milliseconds after the stimulus). If they do not have
the relevant knowledge, the amplitude of the P300 wave will be
significantly smaller.
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MERMER and brain fingerprinting are the brainchildren of
Lawrence Farwell; Farwell has aggressively commercialized the
technology, through his company Brain Fingerprinting Laboratories.
Farwell claims that brain fingerprinting has several advantages over
polygraphy. Because the P300 wave is involuntary, it does not depend
upon the cooperation of the person being tested (with one exception:
the subject must sit still during testing). This makes it especially
suitable for testing people who might be planning a crime, for
instance suspected terrorists. The tester might show them bomb-
making equipment, and examine their EEG. Most importantly, Far-
well claims that brain fingerprinting cannot be manipulated.
Whereas clever criminals can beat conventional lie-detection tests,
they cannot control the involuntary P300 response. In one test of the
technology, subjects were instructed to attempt to conceal the
knowledge being probed; nevertheless the guilty knowledge was
detected. There were no false positives, false negatives or inde-
terminate cases (Farwell and Smith 2001).
Farwell and colleagues claim these kinds of results are a spec-
tacular vindication of brain fingerprinting. However, there are a
number of problems with the technology. First, the method of ana-

lysis used by Farwell is proprietary and undisclosed; for that reason
there cannot be independent testing of its validity. What few tests
there are for brain fingerprinting come exclusively from Farwell’s
laboratory. Independent testing is, of course, the gold standard of
good science; in its absence, we are entitled to a high degree of
scepticism (Wolpe et al. 2005). Moreover, even assuming that Far-
well’s tests have been scrupulously conducted, and his own invest-
ment in the technique has had no effect in biasing his results
(consciously or unconsciously), his sample sizes are too small to
yield a great deal of confidence.
Moreover, there are difficulties concerning the ecological
validity of the technology; that is, the extent to which the findings
can be generalized to the world outside the laboratory. It requires
carefully controlled situations; in particular, it requires that the
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tester possess information that they know will be available only to
the perpetrator of the crime. Sometimes such information will be
known, but probably only in the minority of cases (Tancredi 2004).
Suppose the subject exhibits a P300 to images of a terrorist training
camp. Should we conclude that they have trained as a terrorist, or
that they watch CNN? In fact, in the only field study to date on the
technology, it performed at around chance accuracy (Miyake et al.
1993).
Finally, it appears that Farwell may well be wrong in thinking
that P300 detection methods cannot be beaten. The P300 wave is a
response that is produced when the stimulus is meaningful to the
subject; it can therefore be manufactured by any method that makes
irrelevant probes (used to set the baseline for comparison of wave
amplitude) meaningful to them. Apparently, this is not very difficult.

Rosenfeld and colleagues (Rosenfeld et al. 2004) instructed subjects
to perform a variety of covert acts in response to irrelevant stimuli:
wiggling toes, pressing the fingers of the left hand onto their legs and
imagining the experimenter slapping them in the face. These coun-
termeasures were sufficient to defeat Farwell’s six-probe paradigm.
Reaction-time data remained a significant predictor of guilt on a one-
probe variety of the test, but this is of little comfort to proponents of
MERMER-based guilty knowledge tests; the six-probe test is neces-
sary to avoid too great a rate of false positives as a consequence of
subjects’ finding the probe meaningful for coincidental reasons.
It is apparent that the P300 test has not, so far, lived up to the
hype. However, it may well improve, as the hardware gets more
sophisticated, the algorithms that interpret the data are refined and
the experimenters find better ways to probe guilty knowledge.
Moreover, the P300 test is only one of several deceit-detection
technologies currently under investigation, some of which are also
aimed at detecting lies by reading brains. Langleben and colleagues
(Langleben et al. 2002) used fMRI to scan the brains of subjects
engaged in intentional deceit; they discovered that areas of the
anterior cingulate cortex and the superior frontal gyrus were more
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active during deception than when subjects responded truthfully.
Once again, there are questions concerning the ecological validity of
the technique: how well will it generalize from the controlled
laboratory with willing subjects to the outside world where condi-
tions are uncontrolled and subjects are uncooperative? Even in the
laboratory, the accuracy of the test is not all that high: it showed a
between-group difference between deceivers and the truthful, but the
effect is not great enough to identify individual deceivers with a high

degree of confidence. In addition, technical limitations of fMRI – its
relative lack of spatial and temporal resolution – will probably need
to be overcome before the test has an acceptable degree of reliability.
Once again, however, the technology and the testing methods can
be expected to improve.
What does the foreseeable future hold? I think it is safe to claim
that the kind of mind reading technology which is most feared,
which can scan the brains of subjects and reveal intimate details
about their thoughts, without their knowing that they are under the
mental microscope, is (at least) a long way off. The most promising
methods of mind reading require that we build up a set of data on an
individual subject: we need to establish a baseline for responses we
know to be truthful, against which to compare the probes of interest
(see Illes et al. 2006 for review). Conditions must be carefully con-
trolled and the subject (relatively) cooperative. Moreover, neither
EEG equipment nor, especially, fMRI equipment, is anywhere near
portable or concealable. We can expect to see mind-reading tech-
nology that is of some help in detecting deception in the laboratory,
and that can therefore be used in the kinds of situations in which
polygraphy is employed today, long before we see covert surveillance
of thoughts – if indeed that ever turns out to be possible.
What about laboratory tests for mental states and dispositions
other than lies? Once again, work is proceeding along several fronts.
Many studies have shown brain alterations associated with chronic
schizophrenia; the possibility therefore exists that the disease could
be diagnosed on the basis of brains scans (Farah and Wolpe 2004).
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Some researchers claim to have discovered identifiable neural
correlates of normal personality traits. The work of Canli and col-

leagues (2001; 2002) is the best known and most interesting in this
vein. They found that extraversion was correlated with particular
kinds of responses to images with positive emotional qualities,
whereas neuroticism was associated with differences in responses to
images with negative emotional content. Phelps and colleagues
(2000) used fMRI to study the responses of white subjects to photo-
graphs of black faces. They found a correlation between the degree of
activation of the amygdala and negative evaluation of blacks. We
shall return to this study shortly.
What are the prospects for a genuine mind-reading machine –
one that is capable of interpreting brain states more generally? Out-
side of personality traits, neuroscientists have had some success in
detecting the neural correlates of the orientation of lines to which a
subject is attending (Kamitani and Tong 2005); when subjects viewed
a visually ambiguous figure, the researchers were able, on the basis of
fMRI data, to determine how the subject was resolving the ambi-
guity. Once again, the data was interpretable only after an initial
‘‘training’’ run was used to establish a baseline. Quiroga et al.(2005)
claim to have been able to isolate neural correlates of a much wider
range of representations. In their small study, they apparently
showed that representations of a single person, building, or a single
class of objects – e.g., cartoons from The Simpsons – are encoded in
such a way that, no matter how they are presented, they activate
specific neurons. Thus, one of their subjects had a neuron that
responded preferentially to pictures of Jennifer Aniston, no matter
what angle the picture was taken from, and relatively little to pic-
tures of other people, famous or non-famous. Another subject had a
neuron that responded preferentially to pictures of (what he took to
be) the Sydney Opera House, as well as to the words ‘‘Sydney Opera,’’
but not to other buildings or people.

This study used recordings of single neurons, rather than fMRI
or EEG techniques. It might seem to provide the basis for a much
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more powerful mind-reading technique, since the range of thoughts
that it could detect is far wider than other techniques. However,
there are good reasons to suggest that it will not result in a mind-
reading machine anytime soon. First, the technology is invasive,
requiring electrodes to be implanted deep in the brain (Quiroga and
colleagues were only able to carry out the experiment because they
had a pool of intractable epileptics, who required surgery, to draw on.
Electrodes are implanted in the brains of such subjects to locate the
foci of seizures, to allow the surgeon to locate the precise area for
intervention). Second, once again the technique required training and
cooperation. Even if we could get single neuron recordings from
subjects, we could not interpret them unless we already had a set of
data showing correlations between the firing of the relevant neurons
and particular mental states. Third, the authors themselves caution
that the fact that they were able, in the short time they had available,
to discover pictures to which the individual neurons responded
suggests that the neurons probably respond to other images as well. If
the Jennifer Aniston neuron responded only to pictures of Jen, it
would be nothing short of a miracle that the researchers had been
able to hit upon its precise stimulus. But if the neurons respond
to many different images – and perhaps to sensations and abstract
thoughts as well – then even the possession of single-cell recordings
plus a set of correlation data will not be sufficient to tell us what the
subject is thinking. We may have to conclude that he is thinking
about Jennifer Aniston, or parliamentary democracy, or Friday Night
Football, or a pain in his toe, or something else for which we don’t

have any data yet.
The development of a general mind-reading technology, able to
read the thoughts of people even in the absence of preliminary
training and the establishment of a baseline, is possible only if there
is a great deal of commonality in the neural correlates of mental
states across persons. That is, it will only be possible to construct a
device to read the thoughts of anyone – whether for the purposes of
detecting potential terrorists or selling them cola – if it is possible to
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construct some kind of translation manual, which details the
correlations between particular brain states and particular thoughts.
If my thought that ‘‘elephants are gray’’ has neural correlates which
are very different from your thought that ‘‘elephants are gray’’, then
constructing the translation manual will be difficult or impossible
(impossible if there are few commonalities across the population;
difficult if the differences are tractable – for instance, if there are
identifiable groups, between which the neural correlates of thoughts
differ markedly, but within which there is a great deal of common-
ality – just as there is a great deal of commonality within, but not
between, the vocabularies of different languages).
Moreover, even the construction of a mind-reading machine
reliably able to read the thoughts of a single person, upon whom the
machine has been trained, depends upon our thoughts having stable
neural correlates across time. Perhaps my thought that ‘‘elephants
are gray’’ today has a very similar neural realization to the same
thought, in my head at least, tomorrow, but perhaps next week, or
next month, or next year, it will be quite different.
There is already evidence for some kinds of commonalities
within and across subjects. The method used by Kamitani and Tong

(2005) to detect the orientation of lines to which a subject is
attending uses data from extensive testing of the visual systems of
monkeys; from this data, we know that orientation is represented in
the early stages of visual processing in ways that are consistent
across primates. However, the degree of consistency is not sufficient
to underpin the development of a mind-reading machine: an initial
set of data is necessary to make the neural activity meaningful.
Thanks to the data on the primate visual system, we know where to
look for orientation-tuned neurons, but gathering data on individual
subjects remains indispensable for applying the technique.
It’s not difficult, however, to think of ways in which this data
could be gathered unobtrusively; that is, without the subjects’ being
aware that it is taking place. We could flash lines in such a way
that subjects had their attention attracted to them, and use this
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